Scene classification of high-resolution remote sensing images based on IMFNet
文献类型：期刊论文
| 作者 | X.Zhang; Y.C.Wang; N.Zhang; D.D.Xu; B.Chen; G.L.Ben; X.Wang |
| 刊名 | Journal of Applied Remote Sensing
 |
| 出版日期 | 2019 |
| 卷号 | 13期号:4页码:21 |
| 关键词 | image processing,remote sensing,artificial intelligence,pattern,recognition,scene classification,convolutional neural-networks,deep,Environmental Sciences & Ecology,Remote Sensing,Imaging Science &,Photographic Technology |
| ISSN号 | 1931-3195 |
| DOI | 10.1117/1.Jrs.13.048505 |
| 英文摘要 | Currently, due to the limited amount of data and the difficulty of designing a network, there are few papers on constructing a new convolutional neural network for scene classification using the publicly available datasets of high-resolution remote sensing images. Considering the existing problems, the current scene classification methods of high-resolution remote sensing images are summarized, and the IMFNet model is constructed to classify scenes of high-resolution remote sensing images in this paper. The IMFNet is an end-to-end network, which can learn features from data automatically. The main characteristic of the IMFNet network structure is that the Inception module is used to extract the details of remote sensing images and the multifeature fusion strategy is proposed to ensure the integrity of information. In addition, optimization methods are adopted to improve the classification accuracy. In order to verify the effectiveness of the method proposed in this paper, the two benchmark datasets-the UC Merced dataset and the SIRI-WHU dataset were adopted for experiments. The classification accuracy of the two datasets reaches 92.14% and 90.43%, respectively. Experimental results show that the method proposed has certain advantages over the classification methods based on low-level and middle-level visual features and even some classification methods based on high-level visual features. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) |
| 语种 | 英语 |
| 源URL | [http://ir.ciomp.ac.cn/handle/181722/62782]  |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | X.Zhang,Y.C.Wang,N.Zhang,et al. Scene classification of high-resolution remote sensing images based on IMFNet[J]. Journal of Applied Remote Sensing,2019,13(4):21. |
| APA | X.Zhang.,Y.C.Wang.,N.Zhang.,D.D.Xu.,B.Chen.,...&X.Wang.(2019).Scene classification of high-resolution remote sensing images based on IMFNet.Journal of Applied Remote Sensing,13(4),21. |
| MLA | X.Zhang,et al."Scene classification of high-resolution remote sensing images based on IMFNet".Journal of Applied Remote Sensing 13.4(2019):21. |
入库方式： OAI收割
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。